Instructions to use thoddnn/d3-mini-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thoddnn/d3-mini-q4 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("thoddnn/d3-mini-q4")
config = load_config("thoddnn/d3-mini-q4")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use thoddnn/d3-mini-q4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "thoddnn/d3-mini-q4"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "thoddnn/d3-mini-q4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use thoddnn/d3-mini-q4 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "thoddnn/d3-mini-q4"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default thoddnn/d3-mini-q4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use thoddnn/d3-mini-q4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "thoddnn/d3-mini-q4"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "thoddnn/d3-mini-q4" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

d3-mini-q4 / README.md

thoddnn

Publish self-contained D3 Mini MLX Q4 assets

bb492fe verified about 1 month ago

preview code

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---
license: apache-2.0
base_model: vllm-sr/d3-mini
library_name: mlx
tags:
- mlx
- quantized
- decision-model
- image-text-to-text
---
# D3 Mini Q4 for Fenn / MLX

An MLX 4-bit conversion of [vllm-sr/d3-mini](https://huggingface.co/vllm-sr/d3-mini/tree/a475367de0d8133a3c1a2a0dc12303d89cfa2ab4) for local decision verification on Apple silicon.

This is a decision model, not a generative chat model. It requires the Fenn D3 MLX runtime: stock `mlx_lm.generate`, `mlx_vlm.generate`, or Transformers generation do not implement its readout and attention contract.

## Conversion

- Source revision: `a475367de0d8133a3c1a2a0dc12303d89cfa2ab4`.
- Language projections and embeddings: affine 4-bit, group size 64.
- Vision tower: bfloat16.
- Offset RMS normalization weights and decision readout: float32.
- Full-attention layers are noncausal; DeltaNet recurrence remains causal.
- Last-token pooling, source decision temperature, and code ordering are retained.
- `weights.safetensors` includes the decision readout; no generative language-model head is included.
- `fenn_d3.json` records the upstream provenance and SHA-256 checksums of runtime assets.

The weights and tokenizer/processor files are self-contained; the original full-precision checkpoint is not needed at runtime. Text-only and single-image-plus-text evidence are supported by Fenn's verifier.

## Fenn decisions

Fenn supplies three options in this order: A = MATCH, B = NOT_MATCH, C = UNCERTAIN. Their probabilities are scores conditional on the supplied prompt and options, not calibrated accuracy guarantees. Use the matching runtime and evaluate on your own data before choosing thresholds.
## License and attribution
The original model is by the vLLM Semantic Router Team, based on Qwen3.5. The original Apache-2.0 `LICENSE` and `NOTICE` are preserved. This repository changes the weight representation and packages the assets for Fenn's MLX runtime; it does not claim additional training or improved accuracy.

	---
	license: apache-2.0
	base_model: vllm-sr/d3-mini
	library_name: mlx
	tags:
	- mlx
	- quantized
	- decision-model
	- image-text-to-text
	---

	# D3 Mini Q4 for Fenn / MLX

	An MLX 4-bit conversion of [vllm-sr/d3-mini](https://huggingface.co/vllm-sr/d3-mini/tree/a475367de0d8133a3c1a2a0dc12303d89cfa2ab4) for local decision verification on Apple silicon.

	This is a decision model, not a generative chat model. It requires the Fenn D3 MLX runtime: stock `mlx_lm.generate`, `mlx_vlm.generate`, or Transformers generation do not implement its readout and attention contract.

	## Conversion

	- Source revision: `a475367de0d8133a3c1a2a0dc12303d89cfa2ab4`.
	- Language projections and embeddings: affine 4-bit, group size 64.
	- Vision tower: bfloat16.
	- Offset RMS normalization weights and decision readout: float32.
	- Full-attention layers are noncausal; DeltaNet recurrence remains causal.
	- Last-token pooling, source decision temperature, and code ordering are retained.
	- `weights.safetensors` includes the decision readout; no generative language-model head is included.
	- `fenn_d3.json` records the upstream provenance and SHA-256 checksums of runtime assets.

	The weights and tokenizer/processor files are self-contained; the original full-precision checkpoint is not needed at runtime. Text-only and single-image-plus-text evidence are supported by Fenn's verifier.

	## Fenn decisions

	Fenn supplies three options in this order: A = MATCH, B = NOT_MATCH, C = UNCERTAIN. Their probabilities are scores conditional on the supplied prompt and options, not calibrated accuracy guarantees. Use the matching runtime and evaluate on your own data before choosing thresholds.

	## License and attribution

	The original model is by the vLLM Semantic Router Team, based on Qwen3.5. The original Apache-2.0 `LICENSE` and `NOTICE` are preserved. This repository changes the weight representation and packages the assets for Fenn's MLX runtime; it does not claim additional training or improved accuracy.